code: def _relative_mha(c: TransformerConfigs):
      from labml_nn.transformers.xl.relative_mha import (
          RelativeMultiHeadAttention,
      )
  
      return RelativeMultiHeadAttention(c.n_heads, c.d_model)
  
imports: import copy
  import torch.nn
  from labml.configs import BaseConfigs, option, calculate, aggregate
  from labml_helpers.module import Module
  from .feed_forward import FeedForward
  from .mha import MultiHeadAttention
  from .models import EmbeddingsWithPositionalEncoding, EmbeddingsWithLearnedPositionalEncoding, TransformerLayer, Encoder, Decoder, Generator, EncoderDecoder
label_desc: Use image node_id 5 for calling a global function with example usage: _relative_mha(c) and returns: RelativeMultiHeadAttention